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Keyword contextual targeting

Keyword contextual targeting is similar to PPC, but instead of selecting keywords that match your business, looks at the content of current page a consumer is viewing and serves them ads that are highly relevant to that content. So, viewers only see ads they're interested in and you only serve valuable impressions to right viewers. E.g. Channel 4, Vogue, Reuters, Times ONLINE, Daily Mail & the Guardian serve advertising from Google Adwords content network. Google content network reaches over 75% of unique web surfers in more than 20 languages and over 100+ countries.

Country Unique Reach
  1. Germany 89%
  2. Japan 86%
  3. France 79%
  4. UK 75%
  5. US 76%
  6. Global 75%

Keyword contextual targeting allows you to reach your target audience when they are on websites with content that is related to your products and services. This means you are serving advertisements to a customer group that has a specific interest in your marketplace and is therefore highly targeted.

A recent work by Ribeiro-Neto et. al examines a number of strategies to match pages to ads based on extracted keywords. The ads and pages are represented as vectors in a vector space. The first five strategies proposed in that work match the pages and the ads based on the cosine of the angle between the ad vector and the page vector. To find out the important part of the ad, the authors explore using different ad sections (bid phrase, title, body) as a basis for the ad vector. The winning strategy out of the first five requires the bid phrase to appear on the page and then ranks all such ads by the cosine of the union of all the ad sections and the page vectors.

While both pages and ads are mapped to the same space, there is a discrepancy (impendence mismatch) between the vocabulary used in the ads and in the pages. Furthermore, since in the vector model the dimensions are determined by the number of unique words, plain cosine similarity will not take into account synonyms. To solve this problem, Ribeiro-Neto et al expand the page vocabulary with terms from other similar pages weighted based on the overall similarity of the origin page to the matched page, and show improved matching precision.

In a follow-up work the authors propose a method to learn impact of individual features using genetic programming to produce a matching function. The function is represented as a tree composed of arithmetic operators and the log function as internal nodes, and different numerical features of the query and ad terms as leafs. The results show that genetic programming finds matching functions that significantly improve the keyword contextual targeting compared to the best method (without page side expansion) reported in.

Another approach to keyword contextual targeting is to reduce it to the problem of sponsored search advertising by extracting phrases from the page and matching them with the bid phrase of the ads. In a system for phrase extraction is described that used a variety of features to determine the importance of page phrases for advertising purposes. The system is trained with pages that have been hand annotated with important phrases. The learning algorithm takes into account features based on tf-idf (related to the contextual advertising term: Page weight), html meta data and query logs to detect the most important phrases. During evaluation, each page phrase up to length 5 is considered as potential result and evaluated against a trained classifier. In our work we also experimented with a phrase extractor based on the work reported in. While increasing slightly the precision, it did not change the relative performance of the explored algorithms.

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